Anomaly detection

ABSTRACT

According to an exemplary embodiment of the present disclosure, a computer program stored in a computer readable storage medium is disclosed. The computer program performs operations for processing input data when the computer program is executed by one or more processors of a computer device, the operations including: obtaining input data based on sensor data obtained during manufacturing of an article by using one or more manufacturing recipes in one or more manufacturing equipment; inputting the input data to a neural network model loaded to the computer device; generating an output by processing the input data by using the neural network model; and detecting an anomaly for the input data based on the output of the neural network model.

INCORPORATION BY REFERENCE TO ANY PRIORITY APPLICATIONS

Any and all applications for which a foreign or domestic priority claimis identified in the Application Data Sheet as filed with the presentapplication are hereby incorporated by reference under 37 CFR 1.57.

This application is a continuation of U.S. application Ser. No.16/725,691 which claims priority benefit of U.S. Provisional ApplicationNo. 62/795,690, filed Jan. 23, 2019 and foreign priority to KoreanPatent Application No. 10-2019-0050477, filed Apr. 30, 2019. Each ofthese applications is hereby expressly incorporated by reference in itsentirety.

FIELD

The present disclosure relates to an artificial intelligence technicalfield, and more particularly to anomaly detection utilizing artificialintelligence technology.

BACKGROUND

With the accumulation of sensor data that may be temporarily used orstored in a database and permanently used, research is being conductedon the automated processing of monitoring data for a lot of industrialequipment. In order to implement a data state determination method,research is being conducted on artificial intelligence technology usingan artificial neural network.

A deep learning model utilizing the artificial neural network provides amethod for effectively learning complex nonlinear or dynamic patterns,but when a data to be processed is changed, there have been technicaltasks for updating the model.

Korean Patent Application Laid-Open No. KR1020180055708 discloses amethod of processing an image by utilizing artificial intelligence.

SUMMARY

The present disclosure is conceived in response to the background art,and has been made in an effort to provide a method of processing data byutilizing artificial intelligence.

According to an exemplary embodiment of the present disclosure forimplementing the foregoing object, a computer program stored in acomputer readable storage medium is disclosed. The computer programperforms operations below for processing input data when the computerprogram is executed by one or more processors of a computer device, theoperations including: obtaining input data based on sensor data obtainedduring manufacturing of an article by using one or more manufacturingrecipes in one or more manufacturing equipment; inputting the input datato a neural network model loaded to the computer device; generating anoutput by processing the input data by using the neural network model;and detecting an anomaly for the input data based on the output of theneural network model.

In an alternative exemplary embodiment, the operations may furtherinclude matching a context indicator that associates the input data withat least one of one manufacturing recipe among the one or moremanufacturing recipes and one manufacturing equipment among the one ormore manufacturing equipment with the input data and additionallyinputting the matched context indicator to the neural network model.

In the alternative exemplary embodiment, the neural network model may beconfigured to differently process respective input data based on eachcontext indicator matched with each input data.

In the alternative exemplary embodiment, the neural network model maydifferently process the respective input data by specifying one or allof one manufacturing equipment among the one or more manufacturingequipment and one manufacturing recipe among the one or moremanufacturing recipes based on the each context indicator matched withthe each input data.

In the alternative exemplary embodiment, the context indicator mayinclude a one hot vector including a sparse representation for at leastone of one manufacturing recipe among one or more manufacturing recipesand one manufacturing equipment among one or more manufacturingequipment.

In the alternative exemplary embodiment, the matching of the contextindicator with the input data and the additionally inputting of thematched context indicator to the neural network model may includeinputting the context indicator matched with the input data to an inputlayer or an intermediate layer of the neural network model.

In the alternative exemplary embodiment, the operations may furtherinclude inputting a context indicator to a first preprocessing neuralnetwork model; processing the context indicator by using the firstpreprocessing neural network model; and additionally inputting thepreprocessed context indicator that is an output of the firstpreprocessing neural network model to the neural network model, in whichthe preprocessed context indicator is dense representation of thecontext indicator.

In the alternative exemplary embodiment, the additionally inputting ofthe preprocessed context indicator that is the output of the firstpreprocessing neural network model to the neural network model mayinclude inputting the preprocessed context indicator to an input layeror an intermediate layer of the neural network model.

In the alternative exemplary embodiment, the operations may furtherinclude matching a context characteristic indicator that associates theinput data with at least one of a manufacturing characteristic of onemanufacturing recipe among the one or more manufacturing recipes and amanufacturing characteristic of one manufacturing equipment among theone or more manufacturing equipment with the input data and additionallyinputting the matched context characteristic indicator to the neuralnetwork model.

In the alternative exemplary embodiment, the neural network model may beconfigured to differently process respective input data based on eachcontext characteristic indicator matched with each input data.

In the alternative exemplary embodiment, the neural network model maydifferently process the respective input data based on materialcharacteristic information of the article obtained based on each contextcharacteristic indicator matched with each input data.

In the alternative exemplary embodiment, the context characteristicindicator may include a vector representation for at least one of acharacteristic of one manufacturing recipe among one or moremanufacturing recipes and a characteristic of one manufacturingequipment among the one or more manufacturing equipment.

In the alternative exemplary embodiment, the matching of the contextcharacteristic indicator with the input data and additionally inputtingthe matched context characteristic indicator to the neural network modelmay include inputting the context characteristic indicator matched withthe input data to an input layer or an intermediate layer of the neuralnetwork model.

In the alternative exemplary embodiment, the operations may furtherinclude: inputting a context characteristic indicator to a secondpreprocessing neural network model; processing the contextcharacteristic indicator by using the second preprocessing neuralnetwork model; and additionally inputting the preprocessed contextcharacteristic indicator that is an output of the second preprocessingneural network model to the neural network model, in which thepreprocessed context characteristic indicator may be a denserepresentation of the context characteristic indicator.

In the alternative exemplary embodiment, the additionally inputting ofthe preprocessed context characteristic indicator that is the output ofthe second preprocessing neural network model to the neural networkmodel may include inputting the preprocessed context characteristicindicator to an input layer or an intermediate layer of the neuralnetwork model.

In the alternative exemplary embodiment, the neural network model may bea neural network model capable of processing all or one of encoding anddecoding of input data.

In the alternative exemplary embodiment, the anomaly may include all orone of article anomaly for the article and manufacturing equipmentanomaly for the one or more manufacturing equipment.

In the alternative exemplary embodiment, the anomaly may includemanufacturing anomaly detected by sensor data when the article isproduced in the one or more manufacturing equipment.

In the alternative exemplary embodiment, the neural network model mayinclude a neural network function selected from the group consisting ofan Auto Encoder (AE), a Denoising Auto encoder (DAE), and a VariationalAuto Encoder (VAE).

In the alternative exemplary embodiment, the one or more manufacturingrecipes may include an operation parameter of the manufacturingequipment for producing the article loaded to the one or moremanufacturing equipment.

In the alternative exemplary embodiment, one input data may be formed ofsensor data obtained during manufacturing an article by using onemanufacturing recipe among the one or more manufacturing recipes in onemanufacturing equipment among the one or more manufacturing equipment.

According to another exemplary embodiment of the present disclosure, amethod for processing input data is disclosed. The method may include:obtaining input data based on sensor data obtained during manufacturingof an article by using one or more manufacturing recipes in one or moremanufacturing equipment; inputting the input data to a neural networkmodel loaded to the computer device; generating an output by processingthe input data by using the neural network model; and detecting ananomaly for the input data based on the output of the neural networkmodel.

According to another exemplary embodiment of the present disclosure, acomputer device for processing input data is disclosed. The computerdevice may include: one or more processors; and a memory configured tostore a computer program executable in the one or more processors, inwhich the one or more processors may obtain input data based on sensordata obtained during manufacturing of an article by using one or moremanufacturing recipes in one or more manufacturing equipment; input theinput data to a neural network model loaded to the computer device;generate an output by processing the input data by using the neuralnetwork model; and detect an anomaly for the input data based on theoutput of the neural network model.

The present disclosure may provide a method of processing data by usingartificial intelligence.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an example manufacturing environment inwhich one or more manufacturing equipment produce one or more articles.

FIGS. 2A and 2B are diagrams illustrating an example modeling approachfor anomaly detection in a manufacturing environment in which one ormore manufacturing equipment produce one or more articles.

FIG. 3 is a diagram illustrating a single neural network modelingapproach for anomaly detection in a manufacturing environment in whichone or more equipment produce one or more articles according to anexemplary embodiment of the present disclosure.

FIG. 4 is a block diagram illustrating a computer device for performinga method of anomaly detection according to an exemplary embodiment ofthe present disclosure.

FIG. 5 is a schematic diagram illustrating a network function accordingto an exemplary embodiment of the present disclosure.

FIG. 6 is a schematic diagram illustrating a neural network model foranomaly detection according to a first exemplary embodiment of thepresent disclosure.

FIG. 7 is a schematic diagram illustrating a neural network model foranomaly detection according to a second exemplary embodiment of thepresent disclosure.

FIG. 8 is a schematic diagram illustrating a neural network model foranomaly detection according to a third exemplary embodiment of thepresent disclosure.

FIG. 9 is a schematic diagram illustrating a neural network model foranomaly detection according to a fourth exemplary embodiment of thepresent disclosure.

FIG. 10 is a schematic diagram illustrating a neural network model foranomaly detection according to a fifth exemplary embodiment of thepresent disclosure.

FIG. 11 is a schematic diagram illustrating a neural network model foranomaly detection according to a sixth exemplary embodiment of thepresent disclosure.

FIG. 12 is a schematic diagram illustrating a neural network model foranomaly detection according to a seventh exemplary embodiment of thepresent disclosure.

FIG. 13 is a schematic diagram illustrating a neural network model foranomaly detection according to an eighth exemplary embodiment of thepresent disclosure.

FIG. 14 is a schematic diagram illustrating a neural network model foranomaly detection according to a ninth exemplary embodiment of thepresent disclosure.

FIG. 15 is a schematic diagram illustrating a neural network model foranomaly detection according to a tenth exemplary embodiment of thepresent disclosure.

FIG. 16 is a schematic diagram illustrating a neural network model foranomaly detection according to an eleventh exemplary embodiment of thepresent disclosure.

FIG. 17 is a diagram illustrating an example of a configuration of asystem for anomaly detection according to an exemplary embodiment of thepresent disclosure.

FIG. 18 is a diagram illustrating another example of a configuration ofa system for anomaly detection according to an exemplary embodiment ofthe present disclosure.

FIG. 19 is a diagram illustrating another example of a configuration ofa system for anomaly detection according to an exemplary embodiment ofthe present disclosure.

FIG. 20 is a simple and general schematic diagram of an illustrativecomputing environment, in which the exemplary embodiments of the presentdisclosure may be implemented.

DETAILED DESCRIPTION

Hereinafter, various exemplary embodiments are described with referenceto the drawings. In the present specification, various descriptions arepresented for understanding the present disclosure. However, it isobvious that the exemplary embodiments may be carried out even without aparticular description.

Terms, “component”, “module”, “system”, and the like used in the presentspecification indicate a computer-related entity, hardware, firmware,software, a combination of software and hardware, or execution ofsoftware. For example, a component may be a procedure executed in aprocessor, a processor, an object, an execution thread, a program,and/or a computer, but is not limited thereto. For example, both anapplication executed in a computing device and the computing device maybe components. One or more components may reside within a processorand/or an execution thread. One component may be localized within onecomputer. One component may be distributed between two or morecomputers. Further, the components may be executed by various computerreadable media having various data structures stored therein. Forexample, components may communicate through local and/or remoteprocessing according to a signal (for example, data transmitted toanother system through a network, such as Internet, through data and/ora signal from one component interacting with another component in alocal system and a distributed system) having one or more data packets.

A term “or” intends to mean comprehensive “or”, not exclusive “or”. Thatis, unless otherwise specified or when it is unclear in context, “X usesA or B” intends to mean one of the natural comprehensive substitutions.That is, when X uses A, X uses B, or X uses both A and B, “X uses A orB” may be applied to any one among the cases. Further, a term “and/or”used in the present specification shall be understood to designate andinclude all of the possible combinations of one or more items among thelisted relevant items.

A term “include” and/or “including” shall be understood as meaning thata corresponding characteristic and/or a constituent element exists.Further, a term “include” and/or “including” means that a correspondingcharacteristic and/or a constituent element exists, but it shall beunderstood that the existence or an addition of one or more othercharacteristics, constituent elements, and/or a group thereof is notexcluded. Further, unless otherwise specified or when it is unclear thata single form is indicated in context, the singular shall be construedto generally mean “one or more” in the present specification and theclaims.

Those skilled in the art shall recognize that the various illustrativelogical blocks, configurations, modules, circuits, means, logic, andalgorithm operations described in relation to the exemplary embodimentsadditionally disclosed herein may be implemented by electronic hardware,computer software, or in a combination of electronic hardware andcomputer software. In order to clearly exemplify interchangeability ofhardware and software, the various illustrative components, blocks,configurations, means, logic, modules, circuits, and operations havebeen generally described above in the functional aspects thereof.Whether the functionality is implemented as hardware or software dependson a specific application or design restraints given to the generalsystem. Those skilled in the art may implement the functionalitydescribed by various methods for each of the specific applications.However, it shall not be construed that the determinations of theimplementation deviate from the range of the contents of the presentdisclosure.

The description about the presented exemplary embodiments is provided soas for those skilled in the art to use or carry out the presentinvention. Various modifications of the exemplary embodiments will beapparent to those skilled in the art. General principles defined hereinmay be applied to other exemplary embodiments without departing from thescope of the present disclosure. Accordingly, the present invention isnot limited to the exemplary embodiments presented herein. The presentinvention shall be interpreted within the broadest meaning rangeconsistent to the principles and new characteristics presented herein.

In the present disclosure, a network function, an artificial neuralnetwork, and a neural network may be exchangeably used.

The present specification claims priority to a U.S. Provisional PatentApplication No. 62/795,690 filed in the Korean Industrial PropertyOffice on Jan. 23, 2019, the contents of which are incorporated hereinby reference.

The all of the contents of Korean Patent Application No. 10-2018-0080482filed on Jul. 11, 2018 are incorporated herein by reference.

In the present disclosure, one hot vector has one or more dimensions,and may mean a vector having a coordinate value of 1 for one dimensionand having a coordinate value of 0 for the remaining coordinates. Thatis, the one hot vector may be a vector in which only one component ofthe vector is 1 and the remaining components are 0.

In the present disclosure, a binary vector may be a vector in which allof the components of the vector have a value of 0 or 1.

In the present disclosure, a neural network model may include onenetwork function or an ensemble of one or more network functions.

FIG. 1 is a diagram illustrating an existing manufacturing environmentin which one or more manufacturing equipment produce one or morearticles.

In a general manufacturing environment, one or more manufacturingequipment 20 may produce one or more kinds of article. Each of themanufacturing equipment may produce one or more kinds of article. Forexample, each of the manufacturing equipment 21 and 22 is operated basedon manufacturing recipes 11 and 12 related to an operation parameter ofthe manufacturing equipment for producing the article to produce anarticle, and the operation parameter of the manufacturing equipment maybe changed based on the kind of article to be produced through themanufacturing equipment. A main agent producing an article may producevarious kinds of article in accordance with various needs of customers,and when dedicated manufacturing equipment or a dedicated process isprovided for each kind of the article, cost consumed for a manufacturingprocess is increased, so that various articles may be produced with oneequipment.

FIGS. 2A and 2B are diagrams illustrating an existing modeling approachfor anomaly detection in the existing manufacturing environment in whichone or more manufacturing equipment produce one or more articles.

In a manufacturing environment in which an article is produced, anomalydetection may be performed on manufacturing equipment or an article. Theanomaly detection for the manufacturing equipment may be the detectionfor anomaly (for example, failure of equipment) incurable for themanufacturing equipment. The anomaly detection for the article may bethe detection for anomaly (for example, fault) incurable for thearticle.

FIG. 2A is a diagram illustrating an existing modeling approach foranomaly detection in a manufacturing environment in which one equipmentproduces various articles by various recipes. In FIG. 2A, onemanufacturing equipment 20 may produce one or more kinds of articlebased on the manufacturing recipes 11 and 12 as described above. In thiscase, sensor data obtainable in the manufacturing equipment 20 may havea plurality of normal states. That is, an operation parameter and sensordata of the manufacturing equipment 20 by a first recipe may bedifferent from an operation parameter and sensor data of themanufacturing equipment by a second recipe, and all of the differentoperation parameters and sensor data may be in the normal state.

FIG. 2B is a diagram illustrating an existing modeling approach foranomaly detection in a manufacturing environment in which the pluralityof manufacturing equipment 20 produces articles by one recipe. In theexample of FIG. 2B, the plurality of manufacturing equipment 20 mayproduce articles by one recipe 10. In this case, the plurality ofmanufacturing equipment 20 may have different capabilities, so that theoperation parameters and the sensor data determined by the same recipe10 may be different from each other. For example, the operationparameters of the first manufacturing equipment 21 and the secondmanufacturing equipment 22 for implementing a surface temperature of thesame article may be different from each other. In this case, the sensordata obtainable in the manufacturing equipment 21 and 22 may have aplurality of normal states. That is, the operation parameter and thesensor data of the first manufacturing equipment 21 for themanufacturing recipe 10 may be different from the operation parameterand the sensor data of the second manufacturing equipment 22, and all ofthe different operation parameters and sensor data may be in the normalstate.

When it desires to perform anomaly detection on the plurality of normalstates by using a neural network model 40, in the existing approach, oneneural network model (in this case, one or more network functions withinthe neural network model may be ensembled) performs anomaly detection onone normal state. That is, for the anomaly detection, the plurality ofneural network models 41 and 42 is required for the plurality of normalstates, respectively. That is, the normal state may be determined as ananomaly state based on that one normal state is different from anothernormal state despite the normal state, so that each neural network modelmay be configured so as to process only one normal state. One neuralnetwork model is trained for one normal state, and the plurality ofnormal states exists in the manufacturing environment, so that theanomaly detection is performed on each of the normal states through theneural network models 41 and 42.

In this case, sensor data obtainable in the manufacturing equipment 20may have a plurality of normal states. That is, an operation parameterand sensor data of the manufacturing equipment 20 by a first recipe maybe different from an operation parameter and sensor data of themanufacturing equipment by a second recipe, and all of the differentoperation parameters and sensor data may be in the normal state. When itdesires to perform anomaly detection on the plurality of normal statesby using a neural network model 40, in the existing approach, one neuralnetwork model (in this case, one or more network functions within theneural network model may be ensembled) performs anomaly detection on onenormal state. That is, for the anomaly detection, the plurality ofneural network models 41 and 42 is required for the plurality of normalstates, respectively. One neural network model is trained for one normalstate, and the plurality of normal states exists in the manufacturingenvironment, so that the anomaly detection is performed on each of thenormal states through the neural network models 41 and 42.

FIG. 3 is a diagram illustrating a single neural network modelingapproach for anomaly detection in a manufacturing environment in whichone or more equipment produce one or more articles according to anexemplary embodiment of the present disclosure.

In the case of the existing approach, for anomaly detection for eachmanufacturing recipe and each manufacturing equipment, the neuralnetwork models need to be provided as many as the number of thecombinations of the manufacturing recipes and the manufacturingequipment, so that when the manufacturing equipment and themanufacturing recipe are increased, it is difficult to sufficientlyobtain training data for training the neural network model, and theplurality of neural network models is provided, so that lots of trainingcost of the neural network models may be required. Further, the neuralnetwork models are independently trained, so that knowledge of commoncharacteristics for the respective manufacturing recipes and commoncharacteristics for the respective manufacturing equipment are notshared between the neural network models, so that learning efficiencymay be low.

FIG. 3 illustrates an approach using one neural network model foranomaly detection in a manufacturing environment in which one or moremanufacturing equipment produce one or more articles according to anexemplary embodiment of the present disclosure. As described above,sensor data 30 obtained in a manufacturing environment in which one ormore manufacturing equipment produce one or more articles may have aplurality of normal states.

That is, the sensor data obtained in the case where the firstmanufacturing equipment 21 produces an article based on the first recipe11 may be different from the sensor data obtained in the case where thesecond manufacturing equipment 22 produces an article based on thesecond recipe 12, but both the sensor data may be the normal data. Inthe exemplary embodiment of the present disclosure, the different kindsof normal state may be determined in the sensor data obtained in theplurality of manufacturing equipment 21 and 22 by using one neuralnetwork model. In the exemplary embodiment of the present disclosure,one neural network model 50 may determine a normal state for each of theinput data including the plurality of normal states. The neural networkmodel 50 of the present disclosure may perform the anomaly detectionindependently from a normal pattern change of the input data.

The neural network model 50 of the present disclosure may receive inputdata 61 for the anomaly detection independently from the normal patternchange of the input data, and additionally receive context information62. The context information 62 may include additional meta informationfor the input data. The context information 62 may include informationrelated to how to process the input data matched with the contextinformation 62 in the neural network model, and the neural network model50 may differently process the input data based on the contextinformation 62. More particularly, the neural network model 50 mayobtain a character in a producing environment of the corresponding inputdata based on the context information 62, and process the input dataaccording to the obtained characteristic. For example, the neuralnetwork model 50 may obtain information about manufacturing equipment,information about a manufacturing recipe, information about acharacteristic of an article, information about whether a missing valueexists in the input data, a missing value item, and the like based onthe context information 62, and process the input data based on theadditional information for the processing of the obtained input data.

In the exemplary embodiment of the present disclosure, one neuralnetwork model 50 may include an ensemble of the neural network. That is,in the present disclosure, one neural network model 50 may be configuredof one neural network and may also be configured of an ensemble of oneor more neural networks. That is, one neural network model in thepresent disclosure may be a unified neural network model. When theplurality of neural networks shares knowledge, the neural networks maybe treated as one neural network model, and one neural network model maybe an ensemble configuration of the plurality of neural networks. Whenarchitecture or training data of the neural network is shared, theneural network may be treated as one kind of neural network. Forexample, in the case where a first neural network is trained by using afirst training data set and a second neural network is trained by usinga second training data set (that is, the neural network model isconfigured for each normal state in an existing production environment),the first neural network and the second neural network may form theplurality of neural network models. However, in the exemplary embodimentof the present disclosure, even in the case where one neural networkmodel 50 is configured of one or more neural networks, but architectureof the respective neural networks are common or training data is common,the neural network model may be the ensemble of the neural networks.Accordingly, one neural network model 50 of the present disclosure maybe formed of the ensemble of one or more neural networks sharingknowledge.

The neural network model 50 of the present disclosure may be formed of asupervised learning model 60. In the exemplary embodiment of the presentdisclosure formed of the supervised learning model 60, the supervisedlearning model 60 may receive the input data 61 and the contextinformation 62 as an input of the supervised learning model 60 andoutput anomaly information about the input data 61 as an output. Thesupervised learning model 60 may be trained by using the learning inputdata, the learning context information, and the anomaly informationlabelled to the learning input data and the learning contextinformation.

The neural network model 50 of the present disclosure may be formed ofan unsupervised learning model 65. The unsupervised learning model 65may include a model which is capable of clustering input data. Theunsupervised learning model 65 may be, for example, a model formed of aneural network that restores the input data. The unsupervised learningmodel 65 may include, for example, an auto-encoder. The unsupervisedlearning model 65 may be trained by using learning input data andlearning context information matched with the learning input data.

In the present exemplary embodiment of the present disclosure, dataprocessed by using the neural network model may include all kinds ofdata obtained in an industrial field. For example, the data processed byusing the neural network model may include an operation parameter of adevice for producing a product in a producing process of the product, asensor data obtained by an operation of a device, and the like. Forexample, in the case of a process using temperature setting of equipmentand laser in a specific process, a wavelength of the laser and the likemay be included in the kind of data processed in the present disclosure.For example, the processed data may include lot equipment history datafrom a Management Execution System (MES), data from equipment interfacedata source, processing tool recipes, processing tool test data, probetest data, electric test data, coupling measurement data, diagnosisdata, remote diagnosis data, post-processing data, and the like, but thepresent disclosure is not limited thereto. For more particular example,the processed data may include work-in-process information includingabout 120,000 items for each lot obtained in a semiconductor fab, rawprocessing tool data, equipment interface information, process metrologyinformation (for example, including about 1,000 items for each lot),defect information which a yield-related engineer may access, operationtest information, sort information (including datalog and bitmap), andthe like, but the present disclosure is not limited thereto. Thedescription related to the foregoing kind of data is merely an example,and the present disclosure is not limited thereto. In the exemplaryembodiment of the present disclosure, a computer device 100 maypreprocess collected data. The computer device 100 may supplement amissing value in the collected data. For example, the computer device100 may supplement a missing value with a median value or an averagevalue or may also delete a row in which a plurality of missing valuesexists. Further, for example, in the computer device 100, a subjectmatter expertise of a manager may be utilized in data preprocessing by amatrix completion computer device 100. For example, the computer device100 may remove values (for example, a value estimated as an erroneousoperation of a sensor and the like) completely deviating from a boundaryand a limit from the collected data. Further, the computer device 100may also adjust a value of data so that the data maintains acharacteristic and similarly has a scale. For example, the computerdevice 100 may also apply thermal unit standardization of data. Thecomputer device 100 may also simplify processing by removing heatirrelevant to the anomaly detection from the data. In the exemplaryembodiment of the present disclosure, the computer device 100 mayperform an appropriate input data preprocessing method for easiness ofthe training of the neural network model for generating an anomalydetection model and the anomaly detection. The descriptions of theparticular example of the kind of input data, examples, preprocessing,transformation, and the like are specifically discussed in U.S. patentapplication Ser. No. 10/194,920 (filed on Jul. 12, 2002), the entiretyof which is incorporated herein by reference.

FIG. 4 is a block diagram illustrating a computer device for performinga method of anomaly detection according to an exemplary embodiment ofthe present disclosure.

The configuration of the computer device 100 illustrated in FIG. 4 ismerely a simplified example. In the exemplary embodiment of the presentdisclosure, in the computer device 100, other configurations forperforming a computing environment of the computer device 100 may beincluded, and only a part of the disclosed configurations may alsoconfigure the computer device 100.

The computer device 100 may include a processor 110, a memory 130, and anetwork unit 150.

The processor 110 may be formed of one or more cores, and may include aprocessor for analyzing data and deep learning, such as a CentralProcessing Unit (CPU), a General Purpose Graphics Processing Unit(GPGPU), and a Tensor Processing Unit (TPU) of a computing device. Theprocessor 110 may perform an anomaly detecting method according to anexemplary embodiment of the present disclosure by reading a computerprogram stored in the memory 130. According to the exemplary embodimentof the present disclosure, the processor 110 may perform a calculationfor training a neural network. The processor 110 may perform acalculation, such as processing of input data for learning in DeepLearning (DN), extraction of a feature from input data, errorcalculation, update of a weighted value of a neural network by usingbackpropagation, for training a neural network. At least one of the CPU,GPGPU, and the TPU of the processor 110 may process learning of anetwork function. For example, the CPU and the GPGPU may processlearning of a network function and data classification by using thenetwork function together. Further, in the exemplary embodiment of thepresent disclosure, the learning of the network function and the dataclassification by using the network function may be processed by usingthe processors of the plurality of computer devices together. Further,the computer program executed in the computing device according to theexemplary embodiment of the present disclosure may be a CPU, GPGPU, orTPU executable program.

The computing device 100 in the exemplary embodiment of the presentdisclosure may distribute and process a network function by using atleast one of the CPU, the GPGPU, and the TPU. Further, in the exemplaryembodiment of the present disclosure, the computer device 100 may alsodistribute and process a network function together with another computerdevice. The description for the particular contents related to thedistribution and the processing of the network function of the computerdevice 100 is particularly discussed in U.S. patent application Ser.Nos. 15/161,080 (filed on May 20, 2016) and 15/217,475 (filed on Jul.22, 2016), the entirety of which is incorporated herein by reference.

The processor 110 may obtain input data based on sensor data obtainedduring the manufacturing of an article by using one or moremanufacturing recipes in one or more manufacturing equipment. The inputdata in the exemplary embodiment of the present disclosure may includeall kinds of data obtained in an industrial field. For example, the dataprocessed by using the neural network model may include an operationparameter of a device for producing a product in a producing process ofthe product, sensor data obtained by an operation of a device, and thelike. One input data may include data obtained during the manufacturingof an article by using one manufacturing recipe in one manufacturingequipment. The data obtained during the manufacturing of the article mayinclude sensor data. That is, an input data set including the entireinput data may include the data obtained during the manufacturing of thearticle by using one or more manufacturing recipes in one or moremanufacturing equipment (that is, the data for several manufacturingequipment and several manufacturing recipes may co-exist, so that thedata may have a plurality of normal states), but each input data, whichis the data obtained in the production of the article by onemanufacturing recipe in one manufacturing equipment, may have one normalstate.

In the exemplary embodiment of the present disclosure, the manufacturingequipment may include predetermined manufacturing equipment forproducing an article in an industrial field, and include, for example,semiconductor manufacturing equipment, but the present disclosure is notlimited thereto.

In the exemplary embodiment of the present disclosure, the manufacturingrecipe may be formed of a method of producing an article in anindustrial field, and more particularly, may include data forcontrolling manufacturing equipment. In the exemplary embodiment of thepresent disclosure, the manufacturing recipe may include, for example, asemiconductor manufacturing recipe loaded to manufacturing equipment,but the present disclosure is not limited thereto.

The processor 110 may input the input data to the neural network model50 loaded to the computer device 100. The neural network model 50 may beformed of one neural network model which is capable of performing theanomaly detection on the plurality of normal states as described above.The neural network model may include, for example, a neural networkfunction selected from the group consisting of an Auto Encoder (AE), aDenoising Auto encoder (DAE), a Variational Auto Encoder (VAE), but thepresent disclosure is not limited thereto, and the neural network modelmay include a predetermined neural network function which is capable ofclassifying or clustering the input data. In the exemplary embodiment ofthe present disclosure, in the neural network model, for an additionalinput of a context indicator or a context characteristic indicator, anencoding layer and a decoding layer may be asymmetric (for example, thenumber of nodes of an input layer of the encoding layer may be largerthan the number of nodes of an output layer by the number of items ofthe context indicator or the context characteristic indicator).

In the exemplary embodiment of the present disclosure, the processor 110may match the context indicator 300 which associates the input data 200with at least one of the manufacturing recipe and the manufacturingequipment with the input data and input the context indicator 300 to theneural network model 50.

The context indicator 300 may include information indicating whether theinput data includes the sensor data obtained during the manufacturing ofthe article by a specific manufacturing recipe and/or informationindicating whether the input data includes the sensor data obtainedduring the manufacturing of the article by specific manufacturingequipment. For example, the context indicator may perform a function ofan identifier for at least one of the manufacturing recipe and themanufacturing equipment. The context indicator may include a one hotvector including a sparse representation for at least one of onemanufacturing recipe among one or more manufacturing recipes and onemanufacturing equipment among one or more manufacturing equipment. Forexample, referring to the example illustrated in FIG. 6, in a one hotvector representation 301 of the context indicator, when correspondinginput data is data obtained based on recipe A, in order to representthat the corresponding input data is based on recipe A, the one hotvector representation 301 of the context indicator may be the vectorrepresentation having a value of 1 only for recipe A and having a valueof 0 for the remaining recipes. When the one hot vector representation301 of the context indicator is related to a recipe, the one hot vectorrepresentation 301 of the context indicator may have a value of 1 for amanufacturing recipe of corresponding input data and have a value of 0for the remaining recipes, and when the one hot vector representation301 of the context indicator is related to manufacturing equipment, theone hot vector representation 301 of the context indicator may have avalue of 1 for manufacturing equipment of corresponding input data andhave a value of 0 for the remaining manufacturing equipment. When theone hot vector representation 301 of the context indicator is related tomanufacturing equipment and a recipe, the one hot vector representation301 of the context indicator may have a value of 1 for a recipe andmanufacturing equipment to which corresponding input data corresponds,and a value of 0 for the remainder, and in this case, the contextindicator may be formed of a one hot vector for the manufacturing recipeand a one hot vector for the manufacturing equipment. The description ofthe foregoing one hot vector representation 301 of the context indicatoris merely an example, and the present disclosure is not limited thereto.

The processor 110 may input the context indicator 300 matched with theinput data 200 to a predetermined layer of the neural network model 50.The processor 110 may input the context indicator 300 matched with theinput data 200 to the input layer or an intermediate layer 53 of theneural network model 50. As described above, the neural network model 50may include an auto encoder including an encoding layer 51 which iscapable of performing encoding processing on the input data, and adecoding layer 55 which is capable of performing decoding processing onthe input data. The intermediate layer 53 is a layer located in aconnection portion of the encoding layer 51 and the decoding layer 55and may be the layer having the smallest number of nodes, and may alsobe referred to as a bottleneck layer. The auto encoder model of theexemplary embodiment of the present disclosure may include the encodinglayer 51 performing a reduction of dimensionality (that is, encoding) onthe input data, and a layer performing restoration of dimensionality(that is, decoding) on the input data. The output of the auto encodermay be similar to the input data.

The processor 110 may also preprocess the context indicator 300 andinput the preprocessed context indicator 300 to the neural network model50. The processor 110 may preprocess the context indicator 300 by usinga first preprocessing neural network 310 by inputting the contextindicator 300 to the first preprocessing neural network 310. Theprocessor 110 may also input the context indicator preprocessed by usingthe first preprocessing neural network 310 to the neural network model.The preprocessed context indicator may be a dense representation of thecontext indicator. The preprocessed context indicator may be formed of,for example, a feature of the context indicator. For example, when thefirst preprocessing neural network 310 is an encoder, the preprocessedcontext indicator may be an encoded representation for the contextindicator. The description of the foregoing preprocessed neural networkmodel and dense representation of the preprocessed context indicator aremerely examples, and the present disclosure is not limited thereto.

The processor 110 may input the preprocessed context indicator 300matched with the input data 200 to a predetermined layer of the neuralnetwork model 50. The processor 110 may input the preprocessed contextindicator 300 matched with the input data 200 to the input layer or theintermediate layer 53 of the neural network model 50.

The processor 110 may match a context characteristic indicator 400 whichassociates the input data with at least one of the manufacturingcharacteristic of the manufacturing recipe and the manufacturingcharacteristic of the manufacturing equipment with the input data 200and input the matched context characteristic indicator 400 to the neuralnetwork model 50. The processor 110 may input the context characteristicindicator 400 matched with the input data 200 to a predetermined layerof the neural network model 50.

The context characteristic indicator 400 may include a more detailedrepresentation for representing context of the input data. For example,the context characteristic indicator 400 may include additional detailedinformation about at least one of the manufacturing recipe and themanufacturing equipment. The context characteristic indicator 400 mayinclude more detailed information than the context indicator 300 relatedto the manufacturing recipe, as well as the kind of manufacturingrecipe, or more detailed information than context indicator 300 relatedto the manufacturing equipment, as well as the manufacturing equipment.For example, the context characteristic indicator 400 may includeinformation related to a raw material used in the manufacturing recipe.Further, for example, the context characteristic indicator may includeinformation related to a specification of the manufacturing equipment,the kind of manufacturing parameter, the kind of sensor data measured inthe manufacturing equipment, and the like. Further, for example, thecontext characteristic indicator may include additional informationabout the input data. For example, in the case where a plurality ofitems is present in the input data, the context characteristic indicatormay include information about which item value among the values of theplurality of items is actual data and which item value is a missingvalue.

The context characteristic indicator may include a vector representationfor at least one of one manufacturing recipe among one or moremanufacturing recipes and one manufacturing equipment among one or moremanufacturing equipment. Further, for example, the contextcharacteristic indicator may include a binary vector including alow-dimensional dense representation for at least one of onemanufacturing recipe among one or more manufacturing recipes and onemanufacturing equipment among one or more manufacturing equipment. Asdescribed above, the context characteristic indicator may be formed of,for example, a binary vector representing a characteristic for aspecific manufacturing recipe. Further, as described above, the contextcharacteristic indicator may be formed of, for example, a binary vectorrepresenting a characteristic for specific manufacturing equipment.

For example, the context characteristic indicator may represent acharacteristic of the manufacturing recipe. In this case, for example,the context characteristic indicator may represent whether a specificmaterial is included in the manufacturing recipe, the number of specificmaterials (for example, volume, mass, and the number). Further, forexample, the context characteristic indicator may represent acharacteristic of the manufacturing equipment. In this case, forexample, the context characteristic indicator may represent whether aspecific operation parameter exists in the manufacturing equipment,whether a process is included, a normal operation parameter range in theprocess, and the like. The particular description of the foregoingcontext characteristic indicator for the manufacturing recipe or themanufacturing equipment is merely an example, and the present disclosureis not limited thereto.

The context characteristic indicator represents the characteristic forat least one of the manufacturing recipe and the manufacturingequipment, and in order to represent detailed information about thecharacteristic of the manufacturing recipe and the manufacturingequipment, the context characteristic indicator may have an appropriatepredetermined vector form (for example, a binary vector form in the caseof representing whether a specific item is included, and a real vectorform in the case of representing the number of specific items). Theforegoing vector form of the context characteristic indicator is merelyan example, and the present disclosure is not limited thereto.

For example, referring to the example illustrated in FIG. 10, in abinary vector representation 401 of the context characteristicindicator, the binary vector representation 401 of the contextcharacteristic indicator may represent that a first liquid and gas A areused in recipe A, the first liquid and gas B are used in recipe B, and asecond liquid, gas A, and gas B are used in recipe C. That is, thecontext characteristic indicator 400 is the binary vector, and mayrepresent more detailed information about at least one of themanufacturing recipe and the manufacturing equipment.

For example, referring to the example illustrated in FIG. 16, thecontext characteristic indicator 400 may include a missingcharacteristic indicator 500 representing a missing value. The missingcharacteristic indicator 500 may be a binary vector representing amissing item of the input data so that the neural network model 50 mayidentify which item's value in the input data is an actual value andwhich item's value is a correction value of a missing value with otherdata (for example, in the example of FIG. 16, for the item having avalue of 0, the neural network model 50 may recognize through themissing characteristic indicator whether the actual data is 0 or theitem value is processed as 0 because of a missing value). In the casewhere item values of items 203 of sensor 1 and sensor 2 in first inputdata 201 are actual data and item values of items 205 of sensor 3 andsensor 4 are missing data (for example, in the case where there is nocorresponding sensor in the manufacturing equipment measuring sensordata), a binary vector representation 501 of the missing characteristicindicator in the example of illustrated in FIG. 16 may be a binaryvector having 1 as an item of an actual value like reference numeral 503in the example of FIGS. 16 and 0 as an item of a missing value likereference numeral 505 in the example of FIG. 16. The foregoing binaryvector representation of the missing characteristic indicator is merelyan example, and the present disclosure is not limited thereto.

The processor 110 may input the context characteristic indicator 400matched with the input data 200 to at least one of the input layer orthe intermediate layer 53 of the neural network model 50.

The processor 110 may also preprocess the context characteristicindicator 400 and input the preprocessed context characteristicindicator 400 to the neural network model 50. The processor 110 mayinput the context characteristic indicator 400 to a second preprocessingneural network 410 and preprocess the context characteristic indicator400 by using the second preprocessing neural network 410. The processor110 may input the context characteristic indicator preprocessed by usingthe second preprocessing neural network 410 to the neural network model.The preprocessed context characteristic indicator may be a denserepresentation of the context characteristic indicator. The preprocessedcontext characteristic indicator may be formed of, for example, afeature of the context characteristic indicator. For example, in thecase where the second preprocessing neural network 410 is an encoder,the preprocessed context characteristic indicator may be arepresentation encoded for the context characteristic indicator. Thedescription of the foregoing preprocessed neural network model and denserepresentation of the preprocessed context characteristic indicator aremerely examples, and the present disclosure is not limited thereto.

The processor 110 may input the preprocessed context characteristicindicator 400 matched with the input data 200 to a predetermined layerof the neural network model 50.

The processor 110 may input the preprocessed context characteristicindicator 400 matched with the input data 200 to the input layer or theintermediate layer 53 of the neural network model 50.

The processor 110 may process the input data by using the neural networkmodel and generate an output.

The processor 110 may help the neural network model to differentlyprocess the input data based on at least one of the context indicator300 and the context characteristic indicator 400. The neural networkmodel 50 may differently process the input data according to at leastone of the context indicator 300 and the context characteristicindicator 400 by calculating at least one of the context indicator 300and the context characteristic indicator 400 together with the inputdata. The neural network model 50 calculates at least one of the contextindicator 300 and the context characteristic indicator 400 together withthe input data, so that at least one of the context indicator 300 andthe context characteristic indicator 400 may influence the calculationof the input data by the neural network model, and thus the input datamay be differently processed in the neural network model according to atleast one of the context indicator 300 and the context characteristicindicator 400.

In the exemplary embodiment of the present disclosure, the neuralnetwork model 50 may be configured to differently process each inputdata based on each context indicator matched with each input data. Inthe exemplary embodiment of the present disclosure, the neural networkmodel may specify at least one of the manufacturing equipment and themanufacturing recipe by the context indicator 300. The neural networkmodel may specify one or all of one manufacturing equipment among one ormore manufacturing equipment and one manufacturing recipe among one ormore manufacturing recipes based on each context indicator matched witheach input data. The neural network model specifies at least one of themanufacturing equipment and the manufacturing recipe of the input databased on the context indicator matched with the input data, therebyprocessing the input data based on at least one of the specifiedmanufacturing equipment or manufacturing recipe. The neural networkmodel may associate the input data with at least one of themanufacturing equipment and the manufacturing recipe based on thecontext indicator matched with the input data to process the input data.In the exemplary embodiment of the present disclosure, the associationof the input data with at least one of the manufacturing equipment andthe manufacturing recipe may be based on relevancy between the inputdata trained in the neural network model and the vector representationof the context indicator. That is, in the exemplary embodiment of thepresent disclosure, the neural network model may perform the anomalydetection on the input data having a plurality of normal states of theinput data based on various manufacturing equipment and variousmanufacturing recipes.

In the exemplary embodiment of the present disclosure, the neuralnetwork model 50 may be configured to differently process respectiveinput data based on each context characteristic indicator matched witheach input data. In the exemplary embodiment of the present disclosure,the neural network model may differently process respective input databased on material characteristic information about an article obtainedbased on each context characteristic indicator matched with each inputdata. The neural network model 50 may obtain knowledge for at least oneof a characteristic of the manufacturing equipment and a characteristicof the manufacturing recipe of each input data based on each contextcharacteristic indicator matched with each input data. The neuralnetwork model 50 may differently process the input data based on theknowledge obtained based on the context characteristic indicator.

The neural network model 50 may specify whether each of the itemsincluded in the input data is an actual value or a missing value basedon the missing characteristic indicator 500, and the neural networkmodel 50 may differently process the input data based on the informationabout the identified missing value items.

The processor 110 may detect am anomaly for the input data based onoutput data 210 of the neural network model. The anomaly may include apredetermined anomaly state occurable in the manufacturing environment.In the exemplary embodiment of the present disclosure, for example, theanomaly may include all or one of article anomaly for an article andmanufacturing equipment anomaly for one or more manufacturing equipment.Further, in the exemplary embodiment of the present disclosure, forexample, the anomaly may include anomaly during the manufacturingdetected by sensor data when the article is produced in one or moremanufacturing equipment. Further, in the exemplary embodiment of thepresent disclosure, the anomaly may also include anomaly predictioninformation about the manufacturing equipment.

The neural network model of the present disclosure may include apredetermined neural network model operable so as to generate the inputdata as an output. For example, the neural network model of the presentdisclosure may include an auto encoder capable of restoring input data,a Generative Adversarial Network (GAN) generating a similar output tothe input data, a U-network, a neural network model formed of acombination of a convolutional network and a deconvolutional network,and the like. That is, the neural network model of the presentdisclosure is trained to output output data close to the input data andis trained only with normal data, so that in the case where the inputdata is normal data including no anomaly, the output data may be similarto the input data. In the case where anomaly data is input to the neuralnetwork model of the present disclosure, the neural network model of thepresent disclosure is not trained for the restoration of an anomalypattern, so that the output data may not be similar to the input datacompared to the output when the normal data is received as an input.That is, the pre-trained neural network model of the present disclosuremay detect novelty for an anomaly pattern that is not trained from theinput data, so that the novelty may represent a reconstruction error ofthe output data for the input data. When the reconstruction errorexceeds a predetermined threshold value, the processor 110 may determinethat the input data includes the untrained pattern (that is, the anomalypattern) to detect that the input data includes anomaly.

In the exemplary embodiment of the present disclosure, the neuralnetwork model 50 may be trained to restore the input data 200. Theneural network model 50 may be trained to generate the same output data210 as the input data 200. In the exemplary embodiment of the presentdisclosure, the neural network model 50 may receive at least one of thecontext indicator and the context characteristic indicator in additionto the input data and be trained so as to restore the input data and theadditional input data during the training process.

In another exemplary embodiment of the present disclosure, the neuralnetwork model 50 may be first-trained so as to restore input data, andreceive at least one of the context indicator and the contextcharacteristic indicator in addition to the input data and besecond-trained so as to restore the input data and the additional inputdata.

As described above, according to the exemplary embodiment of the presentdisclosure, in order to perform the anomaly detection independently froma normal pattern change of the input data, the unified neural networkmodel may be utilized for processing the input data having the pluralityof normal states. In the case where the unified neural network isutilized for processing the input data having the plurality of normalstates, it is possible to secure sufficient training data for trainingthe neural network model compared to the case of using the neuralnetwork model separated for each normal pattern, thereby obtaining amore generalized result. Further, by using the unified neural networkmodel, it is possible to prevent inefficiency for the training of themodel occurable in a complex producing environment, and the neuralnetwork model may obtain the correlation between the respective normalstates of the input data and common knowledge in the respective normalstates to obtain more generalized performance.

FIG. 5 is a schematic diagram illustrating a neural network modelaccording to an exemplary embodiment of the present disclosure.

Throughout the present specification, a calculation model, a nervenetwork, a network function, and a neural network may be used as thesame meaning. The neural network may generally be formed of a set ofconnected calculation units referable as “nodes”. The “nodes” may alsobe called “neurons”. The neural network includes one or more nodes. Thenodes (or neurons) forming the neural networks may be connected witheach other by one or more “links”.

Within the neural network, one or more nodes connected through the linkmay relatively form a relationship between an input node and an outputnode. The concept of the input node is relative to the concept of theoutput node, and a predetermined node having an output node relationshipwith respect to one node may have an input node relationship in arelationship with another node, and a reverse relationship is alsoavailable. As described above, the relationship between the input nodeand the output node may be generated based on a link. One or more outputnodes may be connected to one input node through a link, and a reversecase may also be valid.

In the relationship between an input node and an output node connectedthrough one link, a value of the output node may be determined based ondata input to the input node. Herein, a node connecting the input nodeand the output node may have a weight. A weight may be variable, and maybe varied by a user or an algorithm in order to perform a functiondesired by a neural network. For example, when one or more input nodesare connected to one output node by links, respectively, a value of theoutput node may be determined based on values input to the input nodesconnected to the output node and weight set in the link corresponding toeach of the input nodes.

As described above, in the neural network, one or more nodes areconnected through one or more links to form a relationship of an inputnode and an output node within the neural network. A characteristic ofthe neural network may be determined according to the number of nodesand links, a relation between the nodes and the links, and a value of aweight assigned to each of the links within the neural network. Forexample, when there are two neural networks, which have the same numberof nodes and the same number of links and have different weight valuesbetween the links, the two neural networks may be recognized to bedifferent from each other.

The neural network may consist of one or more nodes. Some of the nodesforming the neural network may form one layer based on distances from aninitial input node. For example, a set of nodes having a distance of nfrom an initial input node may form n layers. The distance from theinitial input node may be defined by the minimum number of links, whichneeds to be passed from the initial input node to a corresponding node.However, the definition of the layer is arbitrary for explanation, and adegree of the layer within the neural network may be defined with adifferent method from the foregoing method. For example, the layers ofthe nodes may be defined by a distance from a final output node.

The initial input node may mean one or more nodes, to which data isdirectly input without passing a link in a relationship with other nodesamong the nodes within the neural network. Otherwise, the initial inputnode may mean nodes having no other input node connected through thelinks in a relationship between the nodes based on a link within theneural network. Similarly, the final output node may mean one or morenodes having no output node in the relationship with other nodes amongthe nodes within the neural network. Further, a hidden node may mean anode, not the initial input node and the final output node, forming theneural network. In the neural network in the exemplary embodiment of thepresent disclosure, the number of nodes of the input layer may be thesame as the number of the nodes of the output layer, and the neuralnetwork may have the form in which the number of nodes is decreased andincreased again according to the progress from the input layer to thehidden layer. Further, in the neural network according to anotherexemplary embodiment of the present disclosure, the number of nodes ofthe input layer may be smaller than the number of nodes of the outputlayer, and the neural network may have the form in which the number ofnodes is decreased according to the progress from the input layer to thehidden layer. Further, in the neural network according to anotherexemplary embodiment of the present disclosure, the number of nodes ofthe input layer may be larger than the number of nodes of the outputlayer, and the neural network may have the form in which the number ofnodes is increased according to the progress from the input layer to thehidden layer. The neural network according to another exemplaryembodiment of the present disclosure may have the form in which theforegoing neural networks are combined.

A Deep Neural Network (DNN) may mean a neural network including aplurality of hidden layers, in addition to an input layer and an outputlayer. When the DNN is used, it is possible to recognize a latentstructure of data. That is, it is possible to recognize a latentstructure of a picture, text, a video, a voice, and music (for example,whether a specific object is included in a picture, contents and emotionof the text, and contents and feeling of the voice). The DNN may includea Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN),an auto encoder, Generative Adversarial Networks (GAN), a RestrictedBoltzmann Machine (RBM), a Deep Belief Network (DBN), a Q network, a Unetwork, a Siamese network, and the like. The description of theforegoing DNN is merely an example, and the present disclosure is notlimited thereto.

In the exemplary embodiment of the present disclosure, the neuralnetwork model may also include an auto encoder. The auto encoder may bea kind of artificial neural network for outputting output data similarto input data. The auto encoder may include at least one hidden layer,and the odd number of hidden layers may be disposed between the inputand output layers. The number of nodes in each layer may be reduced fromthe number of nodes in the input layer to an intermediate layer called abottleneck layer (encoding), and then expanded from the bottleneck layerto the output layer (symmetric to the input layer) symmetrically to thereduction. In this case, nodes of a dimensionality reducing layer and adimensionality restoring layer may also be symmetric or may not besymmetric. The auto encoder may perform nonlinear dimensionalityreduction. The number of input layers and the number of output layersmay correspond to the number of sensors left after the preprocessing ofthe input data. In the auto encoder structure, the number of nodes ofthe hidden layer included in the encoder may have a structure thatdecreases away from the input layer. When the number of nodes of thebottleneck layer (the layer having the fewest nodes located between theencoder and the decoder) is too small, the sufficient amount ofinformation may not be transferred, so that the specific number or moreof nodes (for example, a half or more of the nodes of the input layer)may also be maintained.

The neural network may be learned by at least one scheme of supervisedlearning, unsupervised learning, and semi-supervised learning. Thelearning of the neural network is for the purpose of minimizing an errorof an output. In the learning of the neural network, training data isrepeatedly input to the neural network and an error of an output of theneural network for the training data and a target is calculated, and theerror of the neural network is back-propagated in a direction from anoutput layer to an input layer of the neural network in order todecrease the error, and a weight of each node of the neural network isupdated. In the case of the supervised learning, training data labelledwith a correct answer (that is, labelled training data) is used, in eachtraining data, and in the case of the unsupervised learning, a correctanswer may not be labelled to each training data. That is, for example,the training data in the supervised learning for data classification maybe data, in which category is labelled to each of the training data. Thelabelled training data is input to the neural network and the output(category) of the neural network is compared with the label of thetraining data to calculate an error. For another example, in the case ofthe unsupervised learning related to the data classification, trainingdata that is the input is compared with an output of the neural network,so that an error may be calculated. The calculated error isback-propagated in a reverse direction (that is, the direction from theoutput layer to the input layer) in the neural network, and a connectionweight of each of the nodes of the layers of the neural network may beupdated according to the backpropagation. A variation rate of theupdated connection weight of each node may be determined according to alearning rate. The calculation of the neural network for the input dataand the backpropagation of the error may configure a learning epoch. Thelearning rate is differently applicable according to the number of timesof repetition of the learning epoch of the neural network. For example,at the initial stage of the learning of the neural network, a highlearning rate is used to make the neural network rapidly secureperformance of a predetermined level and improve efficiency, and at thelatter stage of the learning, a low learning rate is used to improveaccuracy.

In the learning of the neural network, the training data may begenerally a subset of actual data (that is, data to be processed byusing the learned neural network), and thus an error for the trainingdata is decreased, but there may exist a learning epoch, in which anerror for the actual data is increased. Overfitting is a phenomenon, inwhich the neural network excessively learns training data, so that anerror for actual data is increased. For example, a phenomenon, in whichthe neural network learning a cat while seeing a yellow cat cannotrecognize cats, other than a yellow cat, as cats, is a sort ofoverfitting. Overfitting may act as a reason of increasing an error of amachine learning algorithm. In order to prevent overfitting, variousoptimizing methods may be used. In order to prevent overfitting, amethod of increasing training data, a regularization method, a dropoutmethod of omitting a part of nodes of the network during the learningprocess, and the like may be applied.

FIG. 6 is a schematic diagram illustrating a neural network model foranomaly detection according to a first exemplary embodiment of thepresent disclosure.

In the first exemplary embodiment of the present disclosure, theprocessor 110 may input input data 200 and a context indicator 300 to aninput layer of a neural network model 50. In this case, the neuralnetwork model 50 may utilize information associated with the input databy the context indicator in dimensionality reduction processing (forexample, encoding and feature extraction) on the input data.

As described above, the neural network model 50 may include an encodinglayer 51 performing dimensionality reduction on the input data, adecoding layer 55 performing dimensionality restoration on the inputdata and generating output data in which the input data is restored, andan intermediate layer 53 connected with an encoder and a decoder.

In the first exemplary embodiment of the present disclosure, the neuralnetwork model 50 may include an additional node in the input layer inorder to receive the context indicator in addition to the input data. Inthe neural network model of the first exemplary embodiment of thepresent disclosure, the encoder and the decoder may be asymmetric, andin this case, the neural network of the first exemplary embodiment mayrestore the input data. In this case, output data 210 may be datasimilar to the input data.

In the neural network model of the first exemplary embodiment of thepresent disclosure, the encoder and the decoder may also be asymmetric,and in this case, the neural network of the first exemplary embodimentmay restore the input data and the context indicator. In this case, theoutput data 210 may also be data similar to the input data and thecontext indicator.

The context indicator 300 may be formed of a one hot vector including asparse representation for at least one of one manufacturing recipe amongone or more manufacturing recipes and one manufacturing equipment amongone or more manufacturing equipment, and the one hot vectorrepresentation 301 of the indicator has been described above, so that adescription thereof will be omitted.

FIG. 7 is a schematic diagram illustrating a neural network model foranomaly detection according to a second exemplary embodiment of thepresent disclosure.

In the second exemplary embodiment of the present disclosure, theprocessor 110 may input input data 200 to an input layer andadditionally input a context indicator 300 to an intermediate layer 53of a neural network model 50 or the input layer and the intermediatelayer 53. In this case, the neural network model 50 may utilizecharacteristic information associated with the input data by the contextindicator in dimensionality restoration processing (for example,decoding) or dimensionality restoration processing and dimensionalityreduction processing for the input data.

In the second exemplary embodiment of the present disclosure, the neuralnetwork model 50 may include an additional node in the input layer orthe intermediate layer (or a partial layer of a decoder) in order toreceive the context indicator in addition to the input data. In theneural network model of the second exemplary embodiment of the presentdisclosure, an encoder and the decoder may be asymmetric, and in thiscase, the neural network of the second exemplary embodiment may restorethe input data. In this case, output data 210 may be data similar to theinput data.

In the neural network model of the second exemplary embodiment of thepresent disclosure, the encoder and the decoder may also be asymmetric,and in this case, the neural network of the second exemplary embodimentmay restore the input data and the context indicator. In this case, theoutput data 210 may also be data similar to the input data and thecontext indicator.

FIG. 8 is a schematic diagram illustrating a neural network model foranomaly detection according to a third exemplary embodiment of thepresent disclosure.

In the third exemplary embodiment of the present disclosure, a processor110 may preprocess a context indicator 300 with a first preprocessingneural network 310 and input the preprocessed context indicator to aninput layer of a neural network model 50. In this case, the neuralnetwork model 50 may utilize information associated with input data bythe preprocessed context indicator in dimensionality reductionprocessing (for example, encoding and feature extraction) for the inputdata.

In the third exemplary embodiment of the present disclosure, the contextindicator is preprocessed and then is input to the neural network model50, so that it is possible to lighten the calculation of the neuralnetwork model 50. Further, the preprocessed context indicator is formedof a dense representation of the context indicator, so that theprocessor 110 may more easily process association with at least one of amanufacturing recipe and manufacturing equipment of the input data whenthe input data is calculated by using the neural network model 50.

In the third exemplary embodiment of the present disclosure, the firstpreprocessing neural network 310 may also be trained at the same timewhen the neural network model 50 is trained, and may also be firstseparately trained and then be utilized in the training of the neuralnetwork model 50.

FIG. 9 is a schematic diagram illustrating a neural network model foranomaly detection according to a fourth exemplary embodiment of thepresent disclosure.

In the fourth exemplary embodiment of the present disclosure, aprocessor 110 may preprocess a context indicator 300 with a firstpreprocessing neural network 310 and input the preprocessed contextindicator to an intermediate layer 53 of a neural network model 50 or aninput layer and the intermediate layer 53. In this case, the neuralnetwork model 50 may utilize information associated with input data bythe preprocessed context indicator in dimensionality restorationprocessing (for example, decoding) or dimensionality restorationprocessing and dimensionality reduction processing for the input data.

In the fourth exemplary embodiment of the present disclosure, the firstpreprocessing neural network 310 may also be trained at the same timewhen the neural network model 50 is trained, and may also be firstseparately trained and then be utilized in the training of the neuralnetwork model 50.

FIG. 10 is a schematic diagram illustrating a neural network model foranomaly detection according to a fifth exemplary embodiment of thepresent disclosure.

In the fifth exemplary embodiment of the present disclosure, a processor110 may input input data 200 to an input layer, and additionally input acontext characteristic indicator 400 to the input layer of a neuralnetwork model 50. In this case, the neural network model 50 may utilizeinformation associated with input data by the context characteristicindicator in dimensionality reduction processing (for example, encodingand feature extraction) for the input data.

In the fifth exemplary embodiment of the present disclosure, the neuralnetwork model 50 may include an additional node in the input layer inorder to receive the context characteristic indicator in addition to theinput data. In the neural network model of the fifth exemplaryembodiment of the present disclosure, an encoder and a decoder may beasymmetric, and in this case, the neural network of the fifth exemplaryembodiment may restore the input data. In this case, output data 210 maybe data similar to the input data.

In the neural network model of the fifth exemplary embodiment of thepresent disclosure, the encoder and the decoder may also be symmetric,and in this case, the neural network of the fifth exemplary embodimentmay restore the input data and the context characteristic indicator. Inthis case, the output data 210 may also be data similar to the inputdata and the context characteristic indicator.

FIG. 11 is a schematic diagram illustrating a neural network model foranomaly detection according to a sixth exemplary embodiment of thepresent disclosure.

In the sixth exemplary embodiment of the present disclosure, a processor110 may input input data 200 to an input layer, and additionally input acontext characteristic indicator 400 to an intermediate layer 53 of aneural network model 50 or the input layer and the intermediate layer53. In this case, the neural network model 50 may utilize characteristicinformation associated with input data by the context characteristicindicator in dimensionality restoration processing (for example,decoding) or dimensionality restoration processing and dimensionalityreduction processing for the input data.

In the sixth exemplary embodiment of the present disclosure, the neuralnetwork model 50 may include an additional node in the input layer orthe intermediate layer (or a partial layer of the decoder) in order toreceive the context characteristic indicator in addition to the inputdata. In the neural network model of the sixth exemplary embodiment ofthe present disclosure, an encoder and the decoder may be asymmetric,and in this case, the neural network of the sixth exemplary embodimentmay restore the input data. In this case, output data 210 may be datasimilar to the input.

In the neural network model of the sixth exemplary embodiment of thepresent disclosure, the encoder and the decoder may also be symmetric,and in this case, the neural network of the sixth exemplary embodimentmay restore the input data and the context characteristic indicator. Inthis case, the output data 210 may also be data similar to the inputdata and the context indicator.

FIG. 12 is a schematic diagram illustrating a neural network model foranomaly detection according to a seventh exemplary embodiment of thepresent disclosure.

In the seventh exemplary embodiment of the present disclosure, aprocessor 110 may preprocess a context characteristic indicator 400 witha second preprocessing neural network 410 and input the preprocessedcontext characteristic indicator to an input layer of a neural networkmodel 50. In this case, the neural network model 50 may utilizeinformation associated with input data by the preprocessed contextcharacteristic indicator in dimensionality reduction processing (forexample, encoding and feature extraction) for the input data.

In the seventh exemplary embodiment of the present disclosure, thecontext characteristic indicator is preprocessed and then is input tothe neural network model 50, so that it is possible to lighten thecalculation of the neural network model 50. Further, the preprocessedcontext characteristic indicator is formed of a dense representation ofthe context indicator, so that the processor 110 may more easily processassociation with at least one of a manufacturing recipe andmanufacturing equipment of the input data when the input data iscalculated by using the neural network model 50.

In the seventh exemplary embodiment of the present disclosure, thesecond preprocessing neural network 310 may also be trained at the sametime when the neural network model 50 is trained, and may also be firstseparately trained and then be utilized in the training of the neuralnetwork model 50.

FIG. 13 is a schematic diagram illustrating a neural network model foranomaly detection according to an eighth exemplary embodiment of thepresent disclosure.

In the eighth exemplary embodiment of the present disclosure, aprocessor 110 may preprocess a context characteristic indicator 400 witha second preprocessing neural network 410 and input the preprocessedcontext characteristic indicator to an intermediate layer 53 of a neuralnetwork model 50 or an input layer and the intermediate layer 53. Inthis case, the neural network model 50 may utilize informationassociated with the input data by the preprocessed contextcharacteristic indicator in dimensionality restoration processing (forexample, decoding) or dimensionality restoration processing anddimensionality reduction processing for the input data.

In the eighth exemplary embodiment of the present disclosure, the secondpreprocessing neural network 410 may also be trained at the same timewhen the neural network model 50 is trained, and may also be firstseparately trained and then be utilized in the training of the neuralnetwork model 50.

FIG. 14 is a schematic diagram illustrating a neural network model foranomaly detection according to a ninth exemplary embodiment of thepresent disclosure.

In the ninth exemplary embodiment of the present disclosure, a processor110 may input input data 200 to an input layer, and additionally input acontext indicator 300 and a context characteristic indicator 400 to atleast one of the input layer or an intermediate layer 53 of a neuralnetwork model 50. In this case, both or one of the context indicator 300and the context characteristic indicator 400 may be preprocessed. Thatis, in the ninth exemplary embodiment, the context indicator 300 and thecontext characteristic indicator 400 may be incorporated to be input toat least one of the input layer and the intermediate layer 53 of theneural network model 50. Further, in the ninth exemplary embodiment, thepreprocessed context indicator 300 and the preprocessed contextcharacteristic indicator 400 may be incorporated to be input to at leastone of the input layer and the intermediate layer 53 of the neuralnetwork model 50.

In this case, the neural network model 50 may utilize informationassociated with the input data by the context indicator andcharacteristic information associated with the input data by the contextcharacteristic indicator in dimensionality restoration processing (forexample, decoding) or dimensionality restoration processing anddimensionality reduction processing for the input data.

FIG. 15 is a schematic diagram illustrating a neural network model foranomaly detection according to a tenth exemplary embodiment of thepresent disclosure.

In the tenth exemplary embodiment of the present disclosure, a processor110 may input input data 200 to an input layer, and additionally input acontext indicator 300 and a context characteristic indicator 400 to atleast one of the input layer or an intermediate layer 53 of a neuralnetwork model 50. In this case, both or one of the context indicator 300and a context characteristic indicator 400 may be preprocessed. That is,in the tenth exemplary embodiment of the present disclosure, the contextindicator 300 (or the preprocessed context indicator) and the contextcharacteristic indicator 400 (or the preprocessed context characteristicindicator) may be input to at least one of the input layer and theintermediate layer 53 of the neural network model 50 without beingincorporated with each other unlike the ninth exemplary embodiment. Thatis, in the tenth exemplary embodiment of the present disclosure, boththe context indicator 300 and the context characteristic indicator 400are additionally input to the neural network, but the input positionsthereof may also be different from each other.

In this case, the neural network model 50 may utilize informationassociated with the input data by the context indicator andcharacteristic information associated with the input data by the contextcharacteristic indicator in dimensionality restoration processing (forexample, decoding) or dimensionality restoration processing anddimensionality reduction processing for the input data.

FIG. 16 is a schematic diagram illustrating a neural network model foranomaly detection according to an eleventh exemplary embodiment of thepresent disclosure.

In the eleventh exemplary embodiment of the present disclosure, aprocessor 110 may input a missing characteristic indicator 500 to aninput layer of a neural network model 50. In this case, the neuralnetwork model 50 may determine whether values of items included in inputdata are actual data or missing data by the missing characteristicindicator 500 in the processing of the input data.

FIG. 17 is a diagram illustrating an example of a configuration of asystem for anomaly detection according to an exemplary embodiment of thepresent disclosure.

The system for anomaly detection according to the exemplary embodimentof the present disclosure may include a separate computer devicecommunicable with the manufacturing equipment 20. The neural networkmodel of the exemplary embodiment of the present disclosure may bestored in the separate computer device communicable with themanufacturing equipment 20 and be operated in the computer device. Thecomputer device may communicate with the manufacturing equipment toobtain input data (that is, sensor data), and the obtained input datamay be processed in the computer device to perform the anomalydetection.

FIG. 18 is a diagram illustrating another example of a configuration ofa system for anomaly detection according to an exemplary embodiment ofthe present disclosure.

An anomaly detection method and a computer program according to anexemplary embodiment of the present disclosure may be implemented in acloud computing environment communicable with the manufacturingequipment 20. The neural network model of the exemplary embodiment ofthe present disclosure may be stored and operated in the cloud computingenvironment communicable with the manufacturing equipment 20.

FIG. 19 is a diagram illustrating another example of a configuration ofa system for anomaly detection according to an exemplary embodiment ofthe present disclosure.

An anomaly detection method and a computer program according to anexemplary embodiment of the present disclosure may also be implementedin the manufacturing equipment 20.

FIG. 20 is a simple and general schematic diagram of an illustrativecomputing environment, in which the exemplary embodiments of the presentdisclosure may be implemented.

The present disclosure has been generally described in relation to acomputer executable command executable in one or more computers, butthose skilled in the art will appreciate well that the presentdisclosure is combined with other program modules and/or be implementedby a combination of hardware and software.

In general, a program module includes a routine, a program, a component,a data structure, and the like performing a specific task orimplementing a specific abstract data type. Further, those skilled inthe art will appreciate well that the method of the present disclosuremay be carried out by a personal computer, a hand-held computing device,a microprocessor-based or programmable home appliance (each of which maybe connected with one or more relevant devices and be operated), andother computer system configurations, as well as a single-processor ormultiprocessor computer system, a mini computer, and a main framecomputer.

The exemplary embodiments of the present disclosure may be carried outin a distribution computing environment, in which certain tasks areperformed by remote processing devices connected through a communicationnetwork. In the distribution computing environment, a program module maybe positioned in both a local memory storage device and a remote memorystorage device.

The computer generally includes various computer readable media. Acomputer accessible medium may be a computer readable medium regardlessof the kind of medium, and the computer readable medium includesvolatile and non-volatile media, transitory and non-non-transitorymedia, portable and non-portable media. As a non-limited example, thecomputer readable medium may include a computer readable storage mediumand a computer readable transport medium. The computer readable storagemedium includes volatile and non-volatile media, transitory andnon-non-transitory media, portable and non-portable media constructed bya predetermined method or technology, which stores information, such asa computer readable command, a data structure, a program module, orother data. The computer readable storage medium includes a read onlymemory (RAM), a read only memory (ROM), electrically erasable andprogrammable ROM (EEPROM), a flash memory, or other memory technologies,a compact disc (CD)-ROM, a digital video disk (DVD), or other opticaldisk storage devices, a magnetic cassette, a magnetic tape, a magneticdisk storage device, or other magnetic storage device, or otherpredetermined media, which are accessible by a computer and are used forstoring desired information, but is not limited thereto.

The computer readable transport medium generally includes all of theinformation transport media, such as a carrier wave or other transportmechanisms, which implement a computer readable command, a datastructure, a program module, or other data in a modulated data signal.The modulated data signal means a signal, of which one or more of thecharacteristics are set or changed so as to encode information withinthe signal. As a non-limited example, the computer readable transportmedium includes a wired medium, such as a wired network or adirect-wired connection, and a wireless medium, such as sound, radiofrequency (RF), infrared rays, and other wireless media. A combinationof the predetermined media among the foregoing media is also included ina range of the computer readable transport medium.

An illustrative environment 1100 including a computer 1102 andimplementing several aspects of the present disclosure is illustrated,and the computer 1102 includes a processing device 1104, a system memory1106, and a system bus 1108. The system bus 1108 connects systemcomponents including the system memory 1106 (not limited) to theprocessing device 1104. The processing device 1104 may be apredetermined processor among various common processors. A dualprocessor and other multi-processor architectures may also be used asthe processing device 1104.

The system bus 1108 may be a predetermined one among several types ofbus structure, which may be additionally connectable to a local bususing a predetermined one among a memory bus, a peripheral device bus,and various common bus architectures. The system memory 1106 includes aROM 2110, and a RAM 2112. A basic input/output system (BIOS) is storedin a non-volatile memory 2110, such as a ROM, an erasable andprogrammable ROM (EPROM), and an EEPROM, and the BIOS includes a basicroutine helping a transport of information among the constituentelements within the computer 1102 at a time, such as starting. The RAM2112 may also include a high-rate RAM, such as a static RAM, for cachingdata.

The computer 1102 also includes an embedded hard disk drive (HDD) 2114(for example, enhanced integrated drive electronics (EIDE) and serialadvanced technology attachment (SATA))—the embedded HDD 2114 beingconfigured for outer mounted usage within a proper chassis (notillustrated)—a magnetic floppy disk drive (FDD) 2116 (for example, whichis for reading data from a portable diskette 2118 or recording data inthe portable diskette 2118), and an optical disk drive 1120 (forexample, which is for reading a CD-ROM disk 1122, or reading data fromother high-capacity optical media, such as a DVD, or recording data inthe high-capacity optical media). A hard disk drive 2114, a magneticdisk drive 2116, and an optical disk drive 1120 may be connected to asystem bus 1108 by a hard disk drive interface 1124, a magnetic diskdrive interface 1126, and an optical drive interface 1128, respectively.An interface 1124 for implementing an outer mounted drive includes atleast one of or both a universal serial bus (USB) and the Institute ofElectrical and Electronics Engineers (IMANUFACTURING EQUIPMENT) 1394interface technology.

The drives and the computer readable media associated with the drivesprovide non-volatile storage of data, data structures, computerexecutable commands, and the like. In the case of the computer 1102, thedrive and the medium correspond to the storage of predetermined data inan appropriate digital form. In the description of the computer readablestorage media, the HDD, the portable magnetic disk, and the portableoptical media, such as a CD, or a DVD, are mentioned, but those skilledin the art will appreciate well that other types of compute readablestorage media, such as a zip drive, a magnetic cassette, a flash memorycard, and a cartridge, may also be used in the illustrative operationenvironment, and the predetermined medium may include computerexecutable commands for performing the methods of the presentdisclosure.

A plurality of program modules including an operation system 2130, oneor more application programs 2132, other program modules 2134, andprogram data 2136 may be stored in the drive and the RAM 2112. Anentirety or a part of the operation system, the application, the module,and/or data may also be cached in the RAM 2112. Those skilled in the artwill appreciate well that the present disclosure may be implemented byseveral commercially available operating systems or a combination of theoperating systems.

A user may input a command and information to the computer 1102 throughone or more wired/wireless input devices, for example, a keyboard 1138and a pointing device, such as a mouse 2138. Other input devices (notillustrated) may be a microphone, an IR remote controller, a joystick, agame pad, a stylus pen, a touch screen, and the like. The foregoing andother input devices are frequently connected to the processing device1104 through an input device interface 1142 connected to the system bus1108, but may be connected by other interfaces, such as a parallel port,an IMANUFACTURING EQUIPMENT 1394 serial port, a game port, a USB port,an IR interface, and other interfaces.

A monitor 1144 or other types of display device are also connected tothe system bus 1108 through an interface, such as a video adapter 1146.In addition to the monitor 1144, the computer generally includes otherperipheral output devices (not illustrated), such as a speaker and aprinter.

The computer 1102 may be operated in a networked environment by using alogical connection to one or more remote computers, such as remotecomputer(s) 1148, through wired and/or wireless communication. Theremote computer(s) 1148 may be a workstation, a computing devicecomputer, a router, a personal computer, a portable computer, amicroprocessor-based entertainment device, a peer device, and othergeneral network nodes, and generally includes some or an entirety of theconstituent elements described for the computer 1102, but only a memorystorage device 1150 is illustrated for simplicity. The illustratedlogical connection includes a wired/wireless connection to a local areanetwork (LAN) 1152 and/or a larger network, for example, a wide areanetwork (WAN) 1154. The LAN and WAN networking environments are generalin an office and a company, and make an enterprise-wide computernetwork, such as an Intranet, easy, and all of the LAN and WANnetworking environments may be connected to a worldwide computernetwork, for example, Internet.

When the computer 1102 is used in the LAN networking environment, thecomputer 1102 is connected to the local network 1152 through a wiredand/or wireless communication network interface or an adapter 1156. Theadapter 1156 may make wired or wireless communication to the LAN 1152easy, and the LAN 1152 also includes a wireless access point installedtherein for the communication with the wireless adapter 1156. When thecomputer 1102 is used in the WAN networking environment, the computer1102 may include a modem 1158, is connected to a communication computingdevice on a WAN 1154, or includes other means setting communicationthrough the WAN 1154 via the Internet and the like. The modem 1158,which may be an embedded or outer-mounted and wired or wireless device,is connected to the system bus 1108 through a serial port interface1142. In the networked environment, the program modules described forthe computer 1102 or some of the program modules may be stored in aremote memory/storage device 1150. The illustrated network connection isillustrative, and those skilled in the art will appreciate well thatother means setting a communication link between the computers may beused.

The computer 1102 performs an operation of communicating with apredetermined wireless device or entity, for example, a printer, ascanner, a desktop and/or portable computer, a portable data assistant(PDA), a communication satellite, predetermined equipment or placerelated to a wirelessly detectable tag, and a telephone, which isdisposed by wireless communication and is operated. The operationincludes a wireless fidelity (Wi-Fi) and Bluetooth wireless technologyat least. Accordingly, the communication may have a pre-definedstructure, such as a network in the related art, or may be simply ad hoccommunication between at least two devices.

The Wi-Fi enables a connection to the Internet and the like even withouta wire. The Wi-Fi is a wireless technology, such as a cellular phone,which enables the device, for example, the computer, to transmit andreceive data indoors and outdoors, that is, in any place within acommunication range of a base station. A Wi-Fi network uses a wirelesstechnology, which is called IMANUFACTURING EQUIPMENT 802.11 (a, b, g,etc.) for providing a safe, reliable, and high-rate wireless connection.The Wi-Fi may be used for connecting to the computer, the Internet, andthe wired network (IMANUFACTURING EQUIPMENT 802.3 or Ethernet is used).The Wi-Fi network may be operated at, for example, a data rate of 11Mbps (802.11a) or 54 Mbps (802.11b) in an unauthorized 2.4 and 5 GHzwireless band, or may be operated in a product including both bands(dual bands).

Those skilled in the art may appreciate that information and signals maybe expressed by using predetermined various different technologies andtechniques. For example, data, indications, commands, information,signals, bits, symbols, and chips referable in the foregoing descriptionmay be expressed with voltages, currents, electromagnetic waves,electric fields or particles, optical fields or particles, or apredetermined combination thereof.

Those skilled in the art will appreciate that the various illustrativelogical blocks, modules, processors, means, circuits, and algorithmoperations described in relation to the exemplary embodiments disclosedherein may be implemented by electronic hardware (for convenience,called “software” herein), various forms of program or design code, or acombination thereof. In order to clearly describe compatibility of thehardware and the software, various illustrative components, blocks,modules, circuits, and operations are generally illustrated above inrelation to the functions of the hardware and the software. Whether thefunction is implemented as hardware or software depends on design limitsgiven to a specific application or an entire system. Those skilled inthe art may perform the function described by various schemes for eachspecific application, but it shall not be construed that thedeterminations of the performance depart from the scope of the presentdisclosure.

Various exemplary embodiments presented herein may be implemented by amethod, a device, or a manufactured article using a standard programmingand/or engineering technology. A term “manufactured article” includes acomputer program, a carrier, or a medium accessible from a predeterminedcomputer-readable device. For example, the computer-readable mediumincludes a magnetic storage device (for example, a hard disk, a floppydisk, and a magnetic strip), an optical disk (for example, a CD and aDVD), a smart card, and a flash memory device (for example, an EEPROM, acard, a stick, and a key drive), but is not limited thereto. Further,various storage media presented herein include one or more devicesand/or other machine-readable media for storing information.

It shall be understood that a specific order or a hierarchical structureof the operations included in the presented processes is an example ofillustrative accesses. It shall be understood that a specific order or ahierarchical structure of the operations included in the processes maybe re-arranged within the scope of the present disclosure based ondesign priorities. The accompanying method claims provide variousoperations of elements in a sample order, but it does not mean that theclaims are limited to the presented specific order or hierarchicalstructure.

The description of the presented exemplary embodiments is provided so asfor those skilled in the art to use or carry out the present disclosure.Various modifications of the exemplary embodiments may be apparent tothose skilled in the art, and general principles defined herein may beapplied to other exemplary embodiments without departing from the scopeof the present disclosure. Accordingly, the present disclosure is notlimited to the exemplary embodiments suggested herein, and shall beinterpreted within the broadest meaning range consistent to theprinciples and new characteristics suggested herein.

What is claimed is:
 1. A non-transitory computer readable medium storinga computer program, wherein when the computer program is executed by oneor more processors of a computing device, the computer program performsmethods for processing input data, and the methods include: obtaininginput data based on sensor data obtained during manufacturing of anarticle using one or more manufacturing recipes in a plurality ofmanufacturing equipment or using a plurality of manufacturing recipes inone or more manufacturing equipment; feeding the input data andadditional information for identifying one or more contexts about theinput data into a neural network model loaded on a computer device,wherein the neural network model, including an encoder and a decoder, istrained with the input data and the additional information; furtherfeeding a context characteristic indicator that associates the inputdata with at least one of a characteristic of one manufacturing recipeof the one or more manufacturing recipes or a characteristic of onemanufacturing equipment of the one or more manufacturing equipment, intothe neural network model by matching with the input data; generating anoutput by processing the input data using the neural network model basedon the additional information about the input data, wherein the neuralnetwork model processes the input data for different contexts of theinput data identified by the additional information; and detecting ananomaly for a plurality of normal states corresponding to the input databased on the output of the neural network model.
 2. The non-transitorycomputer readable medium according to claim 1, wherein the methodsfurther include: further feeding a context indicator that associates theinput data with at least one of one manufacturing recipe of the one ormore manufacturing recipes or one manufacturing equipment of the one ormore manufacturing equipment, into the neural network model by matchingwith the input data.
 3. The non-transitory computer readable mediumaccording to claim 2, wherein the neural network model is configured toprocess each input data differently based on the context indicatormatched with the each input data.
 4. The non-transitory computerreadable medium according to claim 3, wherein the neural network modelprocesses the each input data differently by specifying one or all ofone manufacturing equipment of the one or more manufacturing equipmentand one manufacturing recipe of the one or more manufacturing recipes,based on the each context indicator matched with the each input data. 5.The non-transitory computer readable medium according to claim 2,wherein the context indicator includes a one hot vector that includes asparse representation of at least one of one manufacturing recipe of theone or more manufacturing recipes or one manufacturing equipment of theone or more manufacturing equipment.
 6. The non-transitory computerreadable medium according to claim 2, wherein the further feeding thecontext indicator into the neural network model by matching with theinput data includes: feeding the context indicator matched with theinput data into an input layer or an intermediate layer of the neuralnetwork model.
 7. The non-transitory computer readable medium accordingto claim 1, wherein the methods further include: feeding a contextindicator that associates the input data with at least one of acharacteristic of one manufacturing recipe of the one or moremanufacturing recipes or a characteristic of one manufacturing equipmentof the one or more manufacturing equipment, into a first preprocessingneural network model; processing the context indicator using the firstpreprocessing neural network model; and further feeding a preprocessedcontext indicator which is an output of the first preprocessing neuralnetwork model, into the neural network model, wherein the preprocessedcontext indicator is a dense representation of the context indicator. 8.The non-transitory computer readable medium according to claim 7,wherein the further feeding a preprocessed context indicator, which isan output of the first preprocessing neural network model, into theneural network model includes: feeding the preprocessed contextindicator into an input layer or an intermediate layer of the neuralnetwork model.
 9. The non-transitory computer readable medium accordingto claim 1, wherein the neural network model is configured to processeach input data differently based on each context characteristicindicator matched with each input data.
 10. The non-transitory computerreadable medium according to claim 9, wherein the neural network modelprocesses the each input data differently based on materialcharacteristic information of the article that is obtained based on theeach context characteristic indicator matched with the each input data.11. The non-transitory computer readable medium according to claim 1,wherein the context characteristic indicator includes a vectorrepresentation of at least one of a characteristic of one manufacturingrecipe of the one or more manufacturing recipes or a characteristic ofone manufacturing equipment of the one or more manufacturing equipment.12. The non-transitory computer readable medium according to claim 1,wherein the further feeding a context characteristic indicator into theneural network model by matching with the input data, includes: feedingthe context characteristic indicator matched with the input data into aninput layer or an intermediate layer of the neural network model. 13.The non-transitory computer readable medium according to claim 1,wherein the methods further include: feeding a context characteristicindicator that associates the input data with at least one of acharacteristic of one manufacturing recipe of the one or moremanufacturing recipes or a characteristic of one manufacturing equipmentof the one or more manufacturing equipment, into a second preprocessingneural network model; processing the context characteristic indicatorusing the second preprocessing neural network model; and further feedinga preprocessed context characteristic indicator which is an output ofthe second preprocessing neural network model, into the neural networkmodel, wherein the preprocessed context characteristic indicator is adense representation of the context characteristic indicator.
 14. Thenon-transitory computer readable medium according to claim 13, whereinthe further feeding a preprocessed context characteristic indicator,which is an output of the second preprocessing neural network model,into the neural network model includes: feeding the preprocessed contextcharacteristic indicator into an input layer or an intermediate layer ofthe neural network model.
 15. The non-transitory computer readablemedium according to claim 1, wherein the neural network model is aneural network model capable of processing all or one of encoding anddecoding of the input data.
 16. The non-transitory computer readablemedium according to claim 1, wherein the anomaly includes all or one ofan article anomaly of the article and manufacturing equipment anomaly ofthe one or more manufacturing equipment.
 17. The non-transitory computerreadable medium according to claim 1, wherein the anomaly includes amanufacturing anomaly detected by a sensor data when the article isproduced in the one or more manufacturing equipment.
 18. Thenon-transitory computer readable medium according to claim 1, whereinthe neural network model includes a neural network function selectedfrom the group consisting of an AutoEncoder (AE), a DenoisingAutoEncoder (DAE), or a Variational AutoEncoder (VAE).
 19. Thenon-transitory computer readable medium according to claim 1, whereinthe one or more manufacturing recipes includes an operating parameter ofthe manufacturing equipment for producing the article that is loaded onthe one or more manufacturing equipment.
 20. The non-transitory computerreadable medium according to claim 1, wherein one input data comprisessensor data obtained during manufacturing of an article by using onemanufacturing recipe of the one or more manufacturing recipes in onemanufacturing equipment of the one or more manufacturing equipment. 21.A method of processing input data, the method comprising: obtaininginput data based on sensor data obtained during manufacturing of anarticle using one or more manufacturing recipes in a plurality ofmanufacturing equipment or using a plurality of manufacturing recipes inone or more manufacturing equipment; feeding the input data andadditional information for identifying one or more contexts about theinput data into a neural network model loaded on a computer device,wherein the neural network model, including an encoder and a decoder, istrained with the input data and the additional information; furtherfeeding a context characteristic indicator that associates the inputdata with at least one of a characteristic of one manufacturing recipeof the one or more manufacturing recipes or a characteristic of onemanufacturing equipment of the one or more manufacturing equipment, intothe neural network model by matching with the input data; generating anoutput by processing the input data using the neural network model basedon the additional information about the input data, wherein the neuralnetwork model processes the input data for different contexts of theinput data identified by the additional information; and detecting ananomaly for a plurality of normal states corresponding to the input databased on the output of the neural network model.
 22. A computing devicefor processing input data, comprising: one or more processors; and amemory for storing computer programs executable on the one or moreprocessors; wherein the one or more processors are configured to: obtaininput data based on sensor data obtained during manufacturing of anarticle using one or more manufacturing recipes in a plurality ofmanufacturing equipment or using a plurality of manufacturing recipes inone or more manufacturing equipment; feed the input data and additionalinformation for identifying one or more contexts about the input datainto a neural network model loaded on a computer device, wherein theneural network model, including an encoder and a decoder, is trainedwith the input data and the additional information; further feed acontext characteristic indicator that associates the input data with atleast one of a characteristic of one manufacturing recipe of the one ormore manufacturing recipes or a characteristic of one manufacturingequipment of the one or more manufacturing equipment, into the neuralnetwork model by matching with the input data; generate an output byprocessing the input data using the neural network model based on theadditional information about the input data, wherein the neural networkmodel processes the input data for different contexts of the input dataidentified by the additional information; and detect an anomaly for aplurality of normal states corresponding to the input data based on theoutput of the neural network model.